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              <p class="caption" role="heading"><span class="caption-text">Getting Started</span></p>
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<li class="toctree-l1"><a class="reference internal" href="../../getting_started/installation.html">Installation</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../getting_started/jetpack.html">Torch-TensorRT in JetPack</a></li>
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<li class="toctree-l1"><a class="reference internal" href="../../getting_started/capture_and_replay.html">Introduction</a></li>
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<p class="caption" role="heading"><span class="caption-text">User Guide</span></p>
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<li class="toctree-l1"><a class="reference internal" href="../../user_guide/torch_tensorrt_explained.html">Torch-TensorRT Explained</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../user_guide/dynamic_shapes.html">Dynamic shapes with Torch-TensorRT</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../user_guide/saving_models.html">Saving models compiled with Torch-TensorRT</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../user_guide/runtime.html">Deploying Torch-TensorRT Programs</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../user_guide/using_dla.html">DLA</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../user_guide/mixed_precision.html">Compile Mixed Precision models with Torch-TensorRT</a></li>
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<p class="caption" role="heading"><span class="caption-text">Tutorials</span></p>
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<li class="toctree-l1"><a class="reference internal" href="../../tutorials/_rendered_examples/dynamo/torch_compile_advanced_usage.html">Torch Compile Advanced Usage</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../tutorials/_rendered_examples/dynamo/vgg16_ptq.html">Deploy Quantized Models using Torch-TensorRT</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../tutorials/_rendered_examples/dynamo/engine_caching_example.html">Engine Caching</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../tutorials/_rendered_examples/dynamo/engine_caching_bert_example.html">Engine Caching (BERT)</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../tutorials/_rendered_examples/dynamo/refit_engine_example.html">Refitting Torch-TensorRT Programs with New Weights</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../tutorials/serving_torch_tensorrt_with_triton.html">Serving a Torch-TensorRT model with Triton</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../tutorials/_rendered_examples/dynamo/torch_export_cudagraphs.html">Torch Export with Cudagraphs</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../tutorials/_rendered_examples/dynamo/converter_overloading.html">Overloading Torch-TensorRT Converters with Custom Converters</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../tutorials/_rendered_examples/dynamo/custom_kernel_plugins.html">Using Custom Kernels within TensorRT Engines with Torch-TensorRT</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../tutorials/_rendered_examples/dynamo/auto_generate_converters.html">Automatically Generate a Converter for a Custom Kernel</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../tutorials/_rendered_examples/dynamo/auto_generate_plugins.html">Automatically Generate a Plugin for a Custom Kernel</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../tutorials/_rendered_examples/dynamo/mutable_torchtrt_module_example.html">Mutable Torch TensorRT Module</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../tutorials/_rendered_examples/dynamo/weight_streaming_example.html">Weight Streaming</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../tutorials/_rendered_examples/dynamo/pre_allocated_output_example.html">Pre-allocated output buffer</a></li>
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<p class="caption" role="heading"><span class="caption-text">Dynamo Frontend</span></p>
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<li class="toctree-l1"><a class="reference internal" href="../../dynamo/torch_compile.html">TensorRT Backend for <code class="docutils literal notranslate"><span class="pre">torch.compile</span></code></a></li>
<li class="toctree-l1"><a class="reference internal" href="../../dynamo/dynamo_export.html">Compiling Exported Programs with Torch-TensorRT</a></li>
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<p class="caption" role="heading"><span class="caption-text">TorchScript Frontend</span></p>
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<li class="toctree-l1"><a class="reference internal" href="../../ts/creating_torchscript_module_in_python.html">Creating a TorchScript Module</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../ts/creating_torchscript_module_in_python.html#working-with-torchscript-in-python">Working with TorchScript in Python</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../ts/creating_torchscript_module_in_python.html#saving-torchscript-module-to-disk">Saving TorchScript Module to Disk</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../ts/getting_started_with_python_api.html">Using Torch-TensorRT in Python</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../ts/getting_started_with_cpp_api.html">Using Torch-TensorRT in  C++</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../ts/ptq.html">Post Training Quantization (PTQ)</a></li>
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<p class="caption" role="heading"><span class="caption-text">FX Frontend</span></p>
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<li class="toctree-l1"><a class="reference internal" href="../../fx/getting_started_with_fx_path.html">Torch-TensorRT (FX Frontend) User Guide</a></li>
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<p class="caption" role="heading"><span class="caption-text">Model Zoo</span></p>
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<li class="toctree-l1"><a class="reference internal" href="../../tutorials/_rendered_examples/dynamo/torch_compile_resnet_example.html">Compiling ResNet with dynamic shapes using the <cite>torch.compile</cite> backend</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../tutorials/_rendered_examples/dynamo/torch_compile_transformers_example.html">Compiling BERT using the <cite>torch.compile</cite> backend</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../tutorials/_rendered_examples/dynamo/torch_compile_stable_diffusion.html">Compiling Stable Diffusion model using the <cite>torch.compile</cite> backend</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../tutorials/compile_hf_models.html">Compiling LLM models from Huggingface</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../tutorials/_rendered_examples/dynamo/torch_compile_gpt2.html">Compiling GPT2 using the Torch-TensorRT <code class="docutils literal notranslate"><span class="pre">torch.compile</span></code> frontend</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../tutorials/_rendered_examples/dynamo/torch_export_sam2.html">Compiling SAM2 using the dynamo backend</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../tutorials/_rendered_examples/dynamo/torch_export_flux_dev.html">Compiling FLUX.1-dev model using the Torch-TensorRT dynamo backend</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../tutorials/notebooks.html">Legacy notebooks</a></li>
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<p class="caption" role="heading"><span class="caption-text">Python API Documentation</span></p>
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<li class="toctree-l1"><a class="reference internal" href="../../py_api/ptq.html">torch_tensorrt.ts.ptq</a></li>
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<p class="caption" role="heading"><span class="caption-text">C++ API Documentation</span></p>
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<li class="toctree-l1"><a class="reference internal" href="../../_cpp_api/namespace_torch_tensorrt.html">Namespace torch_tensorrt</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../_cpp_api/namespace_torch_tensorrt__logging.html">Namespace torch_tensorrt::logging</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../_cpp_api/namespace_torch_tensorrt__torchscript.html">Namespace torch_tensorrt::torchscript</a></li>
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<p class="caption" role="heading"><span class="caption-text">CLI Documentation</span></p>
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<li class="toctree-l1"><a class="reference internal" href="../../cli/torchtrtc.html">torchtrtc</a></li>
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<p class="caption" role="heading"><span class="caption-text">Contributor Documentation</span></p>
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<li class="toctree-l1"><a class="reference internal" href="../../contributors/system_overview.html">System Overview</a></li>
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  <h1>Source code for torch_tensorrt._compile</h1><div class="highlight"><pre>
<span></span><span class="kn">from</span><span class="w"> </span><span class="nn">__future__</span><span class="w"> </span><span class="kn">import</span> <span class="n">annotations</span>

<span class="kn">import</span><span class="w"> </span><span class="nn">collections.abc</span>
<span class="kn">import</span><span class="w"> </span><span class="nn">logging</span>
<span class="kn">import</span><span class="w"> </span><span class="nn">platform</span>
<span class="kn">import</span><span class="w"> </span><span class="nn">warnings</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">enum</span><span class="w"> </span><span class="kn">import</span> <span class="n">Enum</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">typing</span><span class="w"> </span><span class="kn">import</span> <span class="n">Any</span><span class="p">,</span> <span class="n">Callable</span><span class="p">,</span> <span class="n">List</span><span class="p">,</span> <span class="n">Optional</span><span class="p">,</span> <span class="n">Sequence</span><span class="p">,</span> <span class="n">Set</span><span class="p">,</span> <span class="n">Union</span>

<span class="kn">import</span><span class="w"> </span><span class="nn">torch</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">torch_tensorrt._enums</span><span class="w"> </span><span class="kn">import</span> <span class="n">dtype</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">torch_tensorrt._features</span><span class="w"> </span><span class="kn">import</span> <span class="n">ENABLED_FEATURES</span><span class="p">,</span> <span class="n">needs_cross_compile</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">torch_tensorrt._Input</span><span class="w"> </span><span class="kn">import</span> <span class="n">Input</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">torch_tensorrt.dynamo</span><span class="w"> </span><span class="kn">import</span> <span class="n">_defaults</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">torch_tensorrt.dynamo.runtime._CudaGraphsTorchTensorRTModule</span><span class="w"> </span><span class="kn">import</span> <span class="p">(</span>
    <span class="n">CudaGraphsTorchTensorRTModule</span><span class="p">,</span>
<span class="p">)</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">typing_extensions</span><span class="w"> </span><span class="kn">import</span> <span class="n">TypeGuard</span>

<span class="k">if</span> <span class="n">ENABLED_FEATURES</span><span class="o">.</span><span class="n">fx_frontend</span><span class="p">:</span>
    <span class="kn">import</span><span class="w"> </span><span class="nn">torch.fx</span>
    <span class="kn">from</span><span class="w"> </span><span class="nn">torch_tensorrt.fx</span><span class="w"> </span><span class="kn">import</span> <span class="n">InputTensorSpec</span>
    <span class="kn">from</span><span class="w"> </span><span class="nn">torch_tensorrt.fx.lower</span><span class="w"> </span><span class="kn">import</span> <span class="nb">compile</span> <span class="k">as</span> <span class="n">fx_compile</span>
    <span class="kn">from</span><span class="w"> </span><span class="nn">torch_tensorrt.fx.utils</span><span class="w"> </span><span class="kn">import</span> <span class="n">LowerPrecision</span>

    <span class="n">InputType</span> <span class="o">=</span> <span class="n">Union</span><span class="p">[</span><span class="n">Input</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="n">InputTensorSpec</span><span class="p">]</span>
<span class="k">else</span><span class="p">:</span>
    <span class="n">InputType</span> <span class="o">=</span> <span class="n">Union</span><span class="p">[</span><span class="n">Input</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">]</span>

<span class="k">if</span> <span class="n">ENABLED_FEATURES</span><span class="o">.</span><span class="n">torchscript_frontend</span><span class="p">:</span>
    <span class="kn">import</span><span class="w"> </span><span class="nn">torch_tensorrt.ts</span>
    <span class="kn">from</span><span class="w"> </span><span class="nn">torch_tensorrt.ts._compiler</span><span class="w"> </span><span class="kn">import</span> <span class="nb">compile</span> <span class="k">as</span> <span class="n">torchscript_compile</span>
    <span class="kn">from</span><span class="w"> </span><span class="nn">torch_tensorrt.ts._compiler</span><span class="w"> </span><span class="kn">import</span> <span class="p">(</span>
        <span class="n">convert_method_to_trt_engine</span> <span class="k">as</span> <span class="n">ts_convert_method_to_trt_engine</span><span class="p">,</span>
    <span class="p">)</span>

<span class="k">if</span> <span class="n">ENABLED_FEATURES</span><span class="o">.</span><span class="n">dynamo_frontend</span><span class="p">:</span>
    <span class="kn">from</span><span class="w"> </span><span class="nn">torch.export</span><span class="w"> </span><span class="kn">import</span> <span class="n">ExportedProgram</span>
    <span class="kn">from</span><span class="w"> </span><span class="nn">torch_tensorrt.dynamo._compiler</span><span class="w"> </span><span class="kn">import</span> <span class="nb">compile</span> <span class="k">as</span> <span class="n">dynamo_compile</span>
    <span class="kn">from</span><span class="w"> </span><span class="nn">torch_tensorrt.dynamo._compiler</span><span class="w"> </span><span class="kn">import</span> <span class="p">(</span>
        <span class="n">convert_exported_program_to_serialized_trt_engine</span> <span class="k">as</span> <span class="n">dynamo_convert_exported_program_to_serialized_trt_engine</span><span class="p">,</span>
    <span class="p">)</span>
    <span class="kn">from</span><span class="w"> </span><span class="nn">torch_tensorrt.dynamo._compiler</span><span class="w"> </span><span class="kn">import</span> <span class="p">(</span>
        <span class="n">cross_compile_for_windows</span> <span class="k">as</span> <span class="n">dynamo_cross_compile_for_windows</span><span class="p">,</span>
    <span class="p">)</span>
    <span class="kn">from</span><span class="w"> </span><span class="nn">torch_tensorrt.dynamo._compiler</span><span class="w"> </span><span class="kn">import</span> <span class="p">(</span>
        <span class="n">load_cross_compiled_exported_program</span> <span class="k">as</span> <span class="n">dynamo_load_cross_compiled_exported_program</span><span class="p">,</span>
    <span class="p">)</span>
    <span class="kn">from</span><span class="w"> </span><span class="nn">torch_tensorrt.dynamo._compiler</span><span class="w"> </span><span class="kn">import</span> <span class="p">(</span>
        <span class="n">save_cross_compiled_exported_program</span> <span class="k">as</span> <span class="n">dynamo_save_cross_compiled_exported_program</span><span class="p">,</span>
    <span class="p">)</span>
    <span class="kn">from</span><span class="w"> </span><span class="nn">torch_tensorrt.dynamo._tracer</span><span class="w"> </span><span class="kn">import</span> <span class="n">trace</span> <span class="k">as</span> <span class="n">dynamo_trace</span>

<span class="n">logger</span> <span class="o">=</span> <span class="n">logging</span><span class="o">.</span><span class="n">getLogger</span><span class="p">(</span><span class="vm">__name__</span><span class="p">)</span>

<span class="n">__all__</span> <span class="o">=</span> <span class="p">[</span>
    <span class="s2">&quot;compile&quot;</span><span class="p">,</span>
    <span class="s2">&quot;cross_compile_for_windows&quot;</span><span class="p">,</span>
    <span class="s2">&quot;load_cross_compiled_exported_program&quot;</span><span class="p">,</span>
    <span class="s2">&quot;convert_method_to_trt_engine&quot;</span><span class="p">,</span>
    <span class="s2">&quot;save&quot;</span><span class="p">,</span>
    <span class="s2">&quot;load&quot;</span><span class="p">,</span>
<span class="p">]</span>


<span class="k">def</span><span class="w"> </span><span class="nf">_non_fx_input_interface</span><span class="p">(</span>
    <span class="n">inputs</span><span class="p">:</span> <span class="n">Sequence</span><span class="p">[</span><span class="n">Input</span> <span class="o">|</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">],</span>
<span class="p">)</span> <span class="o">-&gt;</span> <span class="n">TypeGuard</span><span class="p">[</span><span class="n">List</span><span class="p">[</span><span class="n">Input</span> <span class="o">|</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">]]:</span>
    <span class="k">return</span> <span class="nb">all</span><span class="p">(</span><span class="nb">isinstance</span><span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="n">Input</span><span class="p">))</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">inputs</span><span class="p">)</span>


<span class="k">class</span><span class="w"> </span><span class="nc">_IRType</span><span class="p">(</span><span class="n">Enum</span><span class="p">):</span>
<span class="w">    </span><span class="sd">&quot;&quot;&quot;Enum to determine the type of IR selected for model compilation&quot;&quot;&quot;</span>

    <span class="n">ts</span> <span class="o">=</span> <span class="mi">0</span>
    <span class="n">fx</span> <span class="o">=</span> <span class="mi">1</span>
    <span class="n">dynamo</span> <span class="o">=</span> <span class="mi">2</span>
    <span class="n">torch_compile</span> <span class="o">=</span> <span class="mi">3</span>
    <span class="n">exported_program</span> <span class="o">=</span> <span class="mi">4</span>


<span class="k">class</span><span class="w"> </span><span class="nc">_ModuleType</span><span class="p">(</span><span class="n">Enum</span><span class="p">):</span>
<span class="w">    </span><span class="sd">&quot;&quot;&quot;Enum to determine the type of model provided as input&quot;&quot;&quot;</span>

    <span class="n">nn</span> <span class="o">=</span> <span class="mi">0</span>
    <span class="n">ts</span> <span class="o">=</span> <span class="mi">1</span>
    <span class="n">fx</span> <span class="o">=</span> <span class="mi">2</span>
    <span class="n">ep</span> <span class="o">=</span> <span class="mi">3</span>


<span class="k">def</span><span class="w"> </span><span class="nf">_parse_module_type</span><span class="p">(</span><span class="n">module</span><span class="p">:</span> <span class="n">Any</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">_ModuleType</span><span class="p">:</span>
    <span class="k">if</span> <span class="nb">any</span><span class="p">(</span>
        <span class="nb">isinstance</span><span class="p">(</span><span class="n">module</span><span class="p">,</span> <span class="n">t</span><span class="p">)</span>
        <span class="k">for</span> <span class="n">t</span> <span class="ow">in</span> <span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">jit</span><span class="o">.</span><span class="n">ScriptModule</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">jit</span><span class="o">.</span><span class="n">ScriptFunction</span><span class="p">]</span>
    <span class="p">):</span>
        <span class="k">return</span> <span class="n">_ModuleType</span><span class="o">.</span><span class="n">ts</span>
    <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">module</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">fx</span><span class="o">.</span><span class="n">GraphModule</span><span class="p">):</span>
        <span class="k">return</span> <span class="n">_ModuleType</span><span class="o">.</span><span class="n">fx</span>
    <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">module</span><span class="p">,</span> <span class="n">ExportedProgram</span><span class="p">):</span>
        <span class="k">return</span> <span class="n">_ModuleType</span><span class="o">.</span><span class="n">ep</span>
    <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">module</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
        <span class="k">return</span> <span class="n">_ModuleType</span><span class="o">.</span><span class="n">nn</span>
    <span class="k">else</span><span class="p">:</span>
        <span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span><span class="s2">&quot;Module is an unknown format&quot;</span><span class="p">)</span>


<span class="k">def</span><span class="w"> </span><span class="nf">_get_target_fe</span><span class="p">(</span><span class="n">module_type</span><span class="p">:</span> <span class="n">_ModuleType</span><span class="p">,</span> <span class="n">ir</span><span class="p">:</span> <span class="nb">str</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">_IRType</span><span class="p">:</span>
    <span class="n">module_is_tsable</span> <span class="o">=</span> <span class="nb">any</span><span class="p">(</span><span class="n">module_type</span> <span class="o">==</span> <span class="n">t</span> <span class="k">for</span> <span class="n">t</span> <span class="ow">in</span> <span class="p">[</span><span class="n">_ModuleType</span><span class="o">.</span><span class="n">nn</span><span class="p">,</span> <span class="n">_ModuleType</span><span class="o">.</span><span class="n">ts</span><span class="p">])</span>
    <span class="n">module_is_fxable</span> <span class="o">=</span> <span class="nb">any</span><span class="p">(</span><span class="n">module_type</span> <span class="o">==</span> <span class="n">t</span> <span class="k">for</span> <span class="n">t</span> <span class="ow">in</span> <span class="p">[</span><span class="n">_ModuleType</span><span class="o">.</span><span class="n">nn</span><span class="p">,</span> <span class="n">_ModuleType</span><span class="o">.</span><span class="n">fx</span><span class="p">])</span>
    <span class="n">module_is_exportable</span> <span class="o">=</span> <span class="n">module_type</span> <span class="o">==</span> <span class="n">_ModuleType</span><span class="o">.</span><span class="n">ep</span>

    <span class="n">ir_targets_torchscript</span> <span class="o">=</span> <span class="nb">any</span><span class="p">(</span><span class="n">ir</span> <span class="o">==</span> <span class="n">opt</span> <span class="k">for</span> <span class="n">opt</span> <span class="ow">in</span> <span class="p">[</span><span class="s2">&quot;torchscript&quot;</span><span class="p">,</span> <span class="s2">&quot;ts&quot;</span><span class="p">])</span>
    <span class="n">ir_targets_fx</span> <span class="o">=</span> <span class="n">ir</span> <span class="o">==</span> <span class="s2">&quot;fx&quot;</span>
    <span class="n">ir_targets_dynamo</span> <span class="o">=</span> <span class="n">ir</span> <span class="o">==</span> <span class="s2">&quot;dynamo&quot;</span>
    <span class="n">ir_targets_torch_compile</span> <span class="o">=</span> <span class="n">ir</span> <span class="o">==</span> <span class="s2">&quot;torch_compile&quot;</span>

    <span class="k">if</span> <span class="n">module_is_tsable</span> <span class="ow">and</span> <span class="n">ir_targets_torchscript</span><span class="p">:</span>
        <span class="k">if</span> <span class="n">ENABLED_FEATURES</span><span class="o">.</span><span class="n">torchscript_frontend</span><span class="p">:</span>
            <span class="k">return</span> <span class="n">_IRType</span><span class="o">.</span><span class="n">ts</span>
        <span class="k">else</span><span class="p">:</span>
            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
                <span class="s2">&quot;Requested using the TS frontend but the TS frontend is not available in this build of Torch-TensorRT&quot;</span>
            <span class="p">)</span>
    <span class="k">elif</span> <span class="n">module_is_fxable</span> <span class="ow">and</span> <span class="n">ir_targets_fx</span><span class="p">:</span>
        <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span>
            <span class="s2">&quot;FX frontend is deprecated. Please use the Dynamo frontend instead.&quot;</span><span class="p">,</span>
            <span class="ne">DeprecationWarning</span><span class="p">,</span>
            <span class="n">stacklevel</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span>
        <span class="p">)</span>
        <span class="k">if</span> <span class="n">ENABLED_FEATURES</span><span class="o">.</span><span class="n">fx_frontend</span><span class="p">:</span>
            <span class="k">return</span> <span class="n">_IRType</span><span class="o">.</span><span class="n">fx</span>
        <span class="k">else</span><span class="p">:</span>
            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
                <span class="s2">&quot;Requested using the FX frontend but the FX frontend is not available in this build of Torch-TensorRT&quot;</span>
            <span class="p">)</span>
    <span class="k">elif</span> <span class="p">(</span><span class="n">module_is_fxable</span> <span class="ow">or</span> <span class="n">module_is_exportable</span><span class="p">)</span> <span class="ow">and</span> <span class="n">ir_targets_dynamo</span><span class="p">:</span>
        <span class="k">if</span> <span class="n">ENABLED_FEATURES</span><span class="o">.</span><span class="n">dynamo_frontend</span><span class="p">:</span>
            <span class="k">return</span> <span class="n">_IRType</span><span class="o">.</span><span class="n">dynamo</span>
        <span class="k">else</span><span class="p">:</span>
            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
                <span class="s2">&quot;Requested using the Dynamo frontend but the Dynamo frontend is not available in this build of Torch-TensorRT&quot;</span>
            <span class="p">)</span>
    <span class="k">elif</span> <span class="n">module_is_fxable</span> <span class="ow">and</span> <span class="n">ir_targets_torch_compile</span><span class="p">:</span>
        <span class="k">if</span> <span class="n">ENABLED_FEATURES</span><span class="o">.</span><span class="n">dynamo_frontend</span><span class="p">:</span>
            <span class="k">return</span> <span class="n">_IRType</span><span class="o">.</span><span class="n">torch_compile</span>
        <span class="k">else</span><span class="p">:</span>
            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
                <span class="s2">&quot;Requested using the Torch-TensorRT torch.compile backend but the Torch-TensorRT torch.compile backend is not available in this build of Torch-TensorRT&quot;</span>
            <span class="p">)</span>
    <span class="k">else</span><span class="p">:</span>
        <span class="k">if</span> <span class="n">ir</span> <span class="o">==</span> <span class="s2">&quot;default&quot;</span><span class="p">:</span>
            <span class="c1"># Options are listed in order of preference</span>
            <span class="k">if</span> <span class="n">ENABLED_FEATURES</span><span class="o">.</span><span class="n">dynamo_frontend</span> <span class="ow">and</span> <span class="n">module_is_fxable</span><span class="p">:</span>
                <span class="n">logger</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="s2">&quot;ir was set to default, using dynamo frontend&quot;</span><span class="p">)</span>
                <span class="k">return</span> <span class="n">_IRType</span><span class="o">.</span><span class="n">dynamo</span>
            <span class="k">elif</span> <span class="n">ENABLED_FEATURES</span><span class="o">.</span><span class="n">torchscript_frontend</span> <span class="ow">and</span> <span class="n">module_is_tsable</span><span class="p">:</span>
                <span class="k">if</span> <span class="n">ENABLED_FEATURES</span><span class="o">.</span><span class="n">dynamo_frontend</span><span class="p">:</span>
                    <span class="n">logger</span><span class="o">.</span><span class="n">warning</span><span class="p">(</span>
                        <span class="s2">&quot;Input is a torchscript module but the ir was not specified (default=dynamo), please set ir=torchscript to suppress the warning.&quot;</span>
                    <span class="p">)</span>
                <span class="k">return</span> <span class="n">_IRType</span><span class="o">.</span><span class="n">ts</span>
            <span class="k">elif</span> <span class="n">ENABLED_FEATURES</span><span class="o">.</span><span class="n">dynamo_frontend</span> <span class="ow">and</span> <span class="n">module_is_exportable</span><span class="p">:</span>
                <span class="n">logger</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="s2">&quot;ir was set to default, using dynamo frontend&quot;</span><span class="p">)</span>
                <span class="k">return</span> <span class="n">_IRType</span><span class="o">.</span><span class="n">dynamo</span>
            <span class="k">else</span><span class="p">:</span>
                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
                    <span class="sa">f</span><span class="s2">&quot;Module was provided in an unsupported format</span><span class="se">\n</span><span class="s2">Installed frontends:</span><span class="se">\n\t</span><span class="s2">Dynamo - </span><span class="si">{</span><span class="n">ENABLED_FEATURES</span><span class="o">.</span><span class="n">dynamo_frontend</span><span class="si">}</span><span class="se">\n\t</span><span class="s2">TorchScript - </span><span class="si">{</span><span class="n">ENABLED_FEATURES</span><span class="o">.</span><span class="n">torchscript_frontend</span><span class="si">}</span><span class="se">\n\t</span><span class="s2">FX - </span><span class="si">{</span><span class="n">ENABLED_FEATURES</span><span class="o">.</span><span class="n">fx_frontend</span><span class="si">}</span><span class="s2">)&quot;</span>
                <span class="p">)</span>
        <span class="k">else</span><span class="p">:</span>
            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Unknown ir was requested&quot;</span><span class="p">)</span>


<div class="viewcode-block" id="compile"><a class="viewcode-back" href="../../py_api/torch_tensorrt.html#torch_tensorrt.compile">[docs]</a><span class="k">def</span><span class="w"> </span><span class="nf">compile</span><span class="p">(</span>
    <span class="n">module</span><span class="p">:</span> <span class="n">Any</span><span class="p">,</span>
    <span class="n">ir</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;default&quot;</span><span class="p">,</span>
    <span class="n">inputs</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">Sequence</span><span class="p">[</span><span class="n">InputType</span><span class="p">]]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
    <span class="n">arg_inputs</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">Sequence</span><span class="p">[</span><span class="n">Sequence</span><span class="p">[</span><span class="n">Any</span><span class="p">]]]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
    <span class="n">kwarg_inputs</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">dict</span><span class="p">[</span><span class="n">Any</span><span class="p">,</span> <span class="n">Any</span><span class="p">]]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
    <span class="n">enabled_precisions</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">Set</span><span class="p">[</span><span class="n">Union</span><span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">dtype</span><span class="p">,</span> <span class="n">dtype</span><span class="p">]]]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
    <span class="o">**</span><span class="n">kwargs</span><span class="p">:</span> <span class="n">Any</span><span class="p">,</span>
<span class="p">)</span> <span class="o">-&gt;</span> <span class="p">(</span>
    <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span> <span class="o">|</span> <span class="n">torch</span><span class="o">.</span><span class="n">jit</span><span class="o">.</span><span class="n">ScriptModule</span> <span class="o">|</span> <span class="n">torch</span><span class="o">.</span><span class="n">fx</span><span class="o">.</span><span class="n">GraphModule</span> <span class="o">|</span> <span class="n">Callable</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="n">Any</span><span class="p">]</span>
<span class="p">):</span>
<span class="w">    </span><span class="sd">&quot;&quot;&quot;Compile a PyTorch module for NVIDIA GPUs using TensorRT</span>

<span class="sd">    Takes a existing PyTorch module and a set of settings to configure the compiler</span>
<span class="sd">    and using the path specified in ``ir`` lower and compile the module to TensorRT</span>
<span class="sd">    returning a PyTorch Module back</span>

<span class="sd">    Converts specifically the forward method of a Module</span>

<span class="sd">    Arguments:</span>
<span class="sd">        module (Union(torch.nn.Module,torch.jit.ScriptModule): Source module</span>

<span class="sd">    Keyword Arguments:</span>
<span class="sd">        inputs (List[Union(torch_tensorrt.Input, torch.Tensor)]): **Required** List of specifications of input shape, dtype and memory layout for inputs to the module. This argument is required. Input Sizes can be specified as torch sizes, tuples or lists. dtypes can be specified using</span>
<span class="sd">            torch datatypes or torch_tensorrt datatypes and you can use either torch devices or the torch_tensorrt device type enum</span>
<span class="sd">            to select device type. ::</span>

<span class="sd">                inputs=[</span>
<span class="sd">                    torch_tensorrt.Input((1, 3, 224, 224)), # Static NCHW input shape for input #1</span>
<span class="sd">                    torch_tensorrt.Input(</span>
<span class="sd">                        min_shape=(1, 224, 224, 3),</span>
<span class="sd">                        opt_shape=(1, 512, 512, 3),</span>
<span class="sd">                        max_shape=(1, 1024, 1024, 3),</span>
<span class="sd">                        dtype=torch.int32</span>
<span class="sd">                        format=torch.channel_last</span>
<span class="sd">                    ), # Dynamic input shape for input #2</span>
<span class="sd">                    torch.randn((1, 3, 224, 244)) # Use an example tensor and let torch_tensorrt infer settings</span>
<span class="sd">                ]</span>
<span class="sd">        arg_inputs (Tuple[Any, ...]): Same as inputs. Alias for better understanding with kwarg_inputs.</span>
<span class="sd">        kwarg_inputs (dict[Any, ...]): Optional, kwarg inputs to the module forward function.</span>
<span class="sd">        enabled_precision (Set(Union(torch.dtype, torch_tensorrt.dtype))): The set of datatypes that TensorRT can use when selecting kernels</span>
<span class="sd">        ir (str): The requested strategy to compile. (Options: default - Let Torch-TensorRT decide, ts - TorchScript with scripting path)</span>
<span class="sd">        **kwargs: Additional settings for the specific requested strategy (See submodules for more info)</span>

<span class="sd">    Returns:</span>
<span class="sd">        torch.nn.Module: Compiled Module, when run it will execute via TensorRT</span>
<span class="sd">    &quot;&quot;&quot;</span>

    <span class="n">input_list</span> <span class="o">=</span> <span class="n">inputs</span> <span class="k">if</span> <span class="n">inputs</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="k">else</span> <span class="p">[]</span>
    <span class="n">enabled_precisions_set</span><span class="p">:</span> <span class="n">Set</span><span class="p">[</span><span class="n">Union</span><span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">dtype</span><span class="p">,</span> <span class="n">dtype</span><span class="p">]]</span> <span class="o">=</span> <span class="p">(</span>
        <span class="n">enabled_precisions</span>
        <span class="k">if</span> <span class="n">enabled_precisions</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span>
        <span class="k">else</span> <span class="n">_defaults</span><span class="o">.</span><span class="n">ENABLED_PRECISIONS</span>
    <span class="p">)</span>

    <span class="n">module_type</span> <span class="o">=</span> <span class="n">_parse_module_type</span><span class="p">(</span><span class="n">module</span><span class="p">)</span>
    <span class="n">target_ir</span> <span class="o">=</span> <span class="n">_get_target_fe</span><span class="p">(</span><span class="n">module_type</span><span class="p">,</span> <span class="n">ir</span><span class="p">)</span>
    <span class="k">if</span> <span class="n">target_ir</span> <span class="o">==</span> <span class="n">_IRType</span><span class="o">.</span><span class="n">ts</span><span class="p">:</span>
        <span class="n">ts_mod</span> <span class="o">=</span> <span class="n">module</span>
        <span class="k">if</span> <span class="n">module_type</span> <span class="o">==</span> <span class="n">_ModuleType</span><span class="o">.</span><span class="n">nn</span><span class="p">:</span>
            <span class="n">logger</span><span class="o">.</span><span class="n">info</span><span class="p">(</span>
                <span class="s2">&quot;Module was provided as a torch.nn.Module, trying to script the module with torch.jit.script. In the event of a failure please preconvert your module to TorchScript&quot;</span>
            <span class="p">)</span>
            <span class="n">ts_mod</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">jit</span><span class="o">.</span><span class="n">script</span><span class="p">(</span><span class="n">module</span><span class="p">)</span>
        <span class="k">assert</span> <span class="n">_non_fx_input_interface</span><span class="p">(</span><span class="n">input_list</span><span class="p">)</span>
        <span class="n">compiled_ts_module</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">jit</span><span class="o">.</span><span class="n">ScriptModule</span> <span class="o">=</span> <span class="n">torchscript_compile</span><span class="p">(</span>
            <span class="n">ts_mod</span><span class="p">,</span>
            <span class="n">inputs</span><span class="o">=</span><span class="n">input_list</span><span class="p">,</span>
            <span class="n">enabled_precisions</span><span class="o">=</span><span class="n">enabled_precisions_set</span><span class="p">,</span>
            <span class="o">**</span><span class="n">kwargs</span><span class="p">,</span>
        <span class="p">)</span>
        <span class="k">return</span> <span class="n">compiled_ts_module</span>
    <span class="k">elif</span> <span class="n">target_ir</span> <span class="o">==</span> <span class="n">_IRType</span><span class="o">.</span><span class="n">fx</span><span class="p">:</span>
        <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span>
            <span class="s2">&quot;FX frontend is deprecated. Please use the Dynamo frontend instead.&quot;</span><span class="p">,</span>
            <span class="ne">DeprecationWarning</span><span class="p">,</span>
            <span class="n">stacklevel</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span>
        <span class="p">)</span>
        <span class="k">if</span> <span class="ow">not</span> <span class="n">ENABLED_FEATURES</span><span class="o">.</span><span class="n">fx_frontend</span><span class="p">:</span>
            <span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span>
                <span class="s2">&quot;FX frontend is not enabled, cannot compile with target_ir=fx&quot;</span>
            <span class="p">)</span>

        <span class="k">if</span> <span class="p">(</span>
            <span class="n">torch</span><span class="o">.</span><span class="n">float16</span> <span class="ow">in</span> <span class="n">enabled_precisions_set</span>
            <span class="ow">or</span> <span class="n">torch_tensorrt</span><span class="o">.</span><span class="n">dtype</span><span class="o">.</span><span class="n">half</span> <span class="ow">in</span> <span class="n">enabled_precisions_set</span>
        <span class="p">):</span>
            <span class="n">lower_precision</span> <span class="o">=</span> <span class="n">LowerPrecision</span><span class="o">.</span><span class="n">FP16</span>
        <span class="k">elif</span> <span class="p">(</span>
            <span class="n">torch</span><span class="o">.</span><span class="n">float32</span> <span class="ow">in</span> <span class="n">enabled_precisions_set</span>
            <span class="ow">or</span> <span class="n">torch_tensorrt</span><span class="o">.</span><span class="n">dtype</span><span class="o">.</span><span class="n">float</span> <span class="ow">in</span> <span class="n">enabled_precisions_set</span>
        <span class="p">):</span>
            <span class="n">lower_precision</span> <span class="o">=</span> <span class="n">LowerPrecision</span><span class="o">.</span><span class="n">FP32</span>
        <span class="k">else</span><span class="p">:</span>
            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Precision </span><span class="si">{</span><span class="n">enabled_precisions_set</span><span class="si">}</span><span class="s2"> not supported on FX&quot;</span><span class="p">)</span>

        <span class="k">def</span><span class="w"> </span><span class="nf">_fx_input_interface</span><span class="p">(</span>
            <span class="n">inputs</span><span class="p">:</span> <span class="n">Sequence</span><span class="p">[</span><span class="n">Input</span> <span class="o">|</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span> <span class="o">|</span> <span class="n">InputTensorSpec</span><span class="p">],</span>
        <span class="p">)</span> <span class="o">-&gt;</span> <span class="n">TypeGuard</span><span class="p">[</span><span class="n">List</span><span class="p">[</span><span class="n">InputTensorSpec</span> <span class="o">|</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">]]:</span>
            <span class="k">return</span> <span class="nb">all</span><span class="p">(</span><span class="nb">isinstance</span><span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="n">InputTensorSpec</span><span class="p">))</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">inputs</span><span class="p">)</span>

        <span class="k">assert</span> <span class="n">_fx_input_interface</span><span class="p">(</span><span class="n">input_list</span><span class="p">)</span>
        <span class="n">compiled_fx_module</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span> <span class="o">=</span> <span class="n">fx_compile</span><span class="p">(</span>
            <span class="n">module</span><span class="p">,</span>
            <span class="n">input_list</span><span class="p">,</span>
            <span class="n">lower_precision</span><span class="o">=</span><span class="n">lower_precision</span><span class="p">,</span>
            <span class="n">explicit_batch_dimension</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
            <span class="n">dynamic_batch</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
            <span class="o">**</span><span class="n">kwargs</span><span class="p">,</span>
        <span class="p">)</span>
        <span class="k">return</span> <span class="n">compiled_fx_module</span>
    <span class="k">elif</span> <span class="n">target_ir</span> <span class="o">==</span> <span class="n">_IRType</span><span class="o">.</span><span class="n">dynamo</span><span class="p">:</span>
        <span class="c1"># Prepare torch and torchtrt inputs</span>
        <span class="k">if</span> <span class="n">arg_inputs</span> <span class="ow">is</span> <span class="kc">None</span> <span class="ow">and</span> <span class="n">inputs</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
            <span class="k">raise</span> <span class="ne">AssertionError</span><span class="p">(</span><span class="s2">&quot;&#39;arg_inputs&#39; and &#39;inputs&#39; should not both be None.&quot;</span><span class="p">)</span>

        <span class="k">elif</span> <span class="n">arg_inputs</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="n">inputs</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
            <span class="k">raise</span> <span class="ne">AssertionError</span><span class="p">(</span>
                <span class="s2">&quot;&#39;arg_inputs&#39; and &#39;inputs&#39; should not be used at the same time.&quot;</span>
            <span class="p">)</span>
        <span class="k">if</span> <span class="n">inputs</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
            <span class="n">arg_inputs</span> <span class="o">=</span> <span class="n">inputs</span>

        <span class="k">if</span> <span class="n">kwarg_inputs</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
            <span class="n">kwarg_inputs</span> <span class="o">=</span> <span class="p">{}</span>

        <span class="kn">from</span><span class="w"> </span><span class="nn">torch_tensorrt.dynamo.utils</span><span class="w"> </span><span class="kn">import</span> <span class="n">prepare_inputs</span>

        <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">arg_inputs</span><span class="p">,</span> <span class="n">collections</span><span class="o">.</span><span class="n">abc</span><span class="o">.</span><span class="n">Sequence</span><span class="p">):</span>
            <span class="n">arg_inputs</span> <span class="o">=</span> <span class="p">[</span><span class="n">arg_inputs</span><span class="p">]</span>  <span class="c1"># type: ignore</span>

        <span class="c1"># Export the module</span>
        <span class="n">torchtrt_arg_inputs</span> <span class="o">=</span> <span class="n">prepare_inputs</span><span class="p">(</span><span class="n">arg_inputs</span><span class="p">)</span>
        <span class="n">torchtrt_kwarg_inputs</span> <span class="o">=</span> <span class="n">prepare_inputs</span><span class="p">(</span><span class="n">kwarg_inputs</span><span class="p">)</span>

        <span class="n">exp_program</span> <span class="o">=</span> <span class="n">dynamo_trace</span><span class="p">(</span>
            <span class="n">module</span><span class="p">,</span> <span class="n">torchtrt_arg_inputs</span><span class="p">,</span> <span class="n">kwarg_inputs</span><span class="o">=</span><span class="n">torchtrt_kwarg_inputs</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span>
        <span class="p">)</span>
        <span class="n">trt_graph_module</span> <span class="o">=</span> <span class="n">dynamo_compile</span><span class="p">(</span>
            <span class="n">exp_program</span><span class="p">,</span>
            <span class="n">arg_inputs</span><span class="o">=</span><span class="n">torchtrt_arg_inputs</span><span class="p">,</span>
            <span class="n">enabled_precisions</span><span class="o">=</span><span class="n">enabled_precisions_set</span><span class="p">,</span>
            <span class="o">**</span><span class="n">kwargs</span><span class="p">,</span>
        <span class="p">)</span>
        <span class="k">return</span> <span class="n">trt_graph_module</span>
    <span class="k">elif</span> <span class="n">target_ir</span> <span class="o">==</span> <span class="n">_IRType</span><span class="o">.</span><span class="n">torch_compile</span><span class="p">:</span>
        <span class="k">return</span> <span class="n">torch_compile</span><span class="p">(</span>
            <span class="n">module</span><span class="p">,</span> <span class="n">enabled_precisions</span><span class="o">=</span><span class="n">enabled_precisions_set</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span>
        <span class="p">)</span>
    <span class="k">else</span><span class="p">:</span>
        <span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span><span class="s2">&quot;Module is an unknown format or the ir requested is unknown&quot;</span><span class="p">)</span></div>


<span class="nd">@needs_cross_compile</span>
<span class="k">def</span><span class="w"> </span><span class="nf">cross_compile_for_windows</span><span class="p">(</span>
    <span class="n">module</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">,</span>
    <span class="n">file_path</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span>
    <span class="n">inputs</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">Sequence</span><span class="p">[</span><span class="n">Input</span> <span class="o">|</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">]]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
    <span class="n">arg_inputs</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">Sequence</span><span class="p">[</span><span class="n">Sequence</span><span class="p">[</span><span class="n">Any</span><span class="p">]]]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
    <span class="n">kwarg_inputs</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">dict</span><span class="p">[</span><span class="n">Any</span><span class="p">,</span> <span class="n">Any</span><span class="p">]]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
    <span class="n">enabled_precisions</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">Set</span><span class="p">[</span><span class="n">Union</span><span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">dtype</span><span class="p">,</span> <span class="n">dtype</span><span class="p">]]]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
    <span class="o">**</span><span class="n">kwargs</span><span class="p">:</span> <span class="n">Any</span><span class="p">,</span>
<span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
<span class="w">    </span><span class="sd">&quot;&quot;&quot;Compile a PyTorch module using TensorRT in Linux for Inference in Windows</span>

<span class="sd">    Takes an existing PyTorch module and a set of settings to configure the compiler</span>
<span class="sd">    and it will convert methods to AOT graphs which call equivalent TensorRT serialized</span>
<span class="sd">    engine info into the disk in the specified file_path user provided.</span>
<span class="sd">    It will then allow user to load the deserialized model from the disk in Windows.</span>
<span class="sd">    Note: the model cross compiled for windows in Linux environmen can only be loaded</span>
<span class="sd">    in Windows.</span>

<span class="sd">    Argument:</span>
<span class="sd">        module (torch.nn.Module): Source module</span>
<span class="sd">        file_path (str): the file path to store the serialized module into the disk</span>

<span class="sd">    Keyword Arguments:</span>
<span class="sd">        inputs (List[Union(torch_tensorrt.Input, torch.Tensor)]): **Required** List of specifications of input shape, dtype and memory layout for inputs to the module. This argument is required. Input Sizes can be specified as torch sizes, tuples or lists. dtypes can be specified using</span>
<span class="sd">            torch datatypes or torch_tensorrt datatypes and you can use either torch devices or the torch_tensorrt device type enum</span>
<span class="sd">            to select device type. ::</span>

<span class="sd">                inputs=[</span>
<span class="sd">                    torch_tensorrt.Input((1, 3, 224, 224)), # Static NCHW input shape for input #1</span>
<span class="sd">                    torch_tensorrt.Input(</span>
<span class="sd">                        min_shape=(1, 224, 224, 3),</span>
<span class="sd">                        opt_shape=(1, 512, 512, 3),</span>
<span class="sd">                        max_shape=(1, 1024, 1024, 3),</span>
<span class="sd">                        dtype=torch.int32</span>
<span class="sd">                        format=torch.channel_last</span>
<span class="sd">                    ), # Dynamic input shape for input #2</span>
<span class="sd">                    torch.randn((1, 3, 224, 244)) # Use an example tensor and let torch_tensorrt infer settings</span>
<span class="sd">                ]</span>
<span class="sd">        arg_inputs (Tuple[Any, ...]): Same as inputs. Alias for better understanding with kwarg_inputs.</span>
<span class="sd">        kwarg_inputs (dict[Any, ...]): Optional, kwarg inputs to the module forward function.</span>
<span class="sd">        enabled_precision (Set(Union(torch.dtype, torch_tensorrt.dtype))): The set of datatypes that TensorRT can use when selecting kernels</span>
<span class="sd">        **kwargs: Additional settings for the specific requested strategy (See submodules for more info)</span>

<span class="sd">    &quot;&quot;&quot;</span>

    <span class="k">if</span> <span class="n">platform</span><span class="o">.</span><span class="n">system</span><span class="p">()</span> <span class="o">!=</span> <span class="s2">&quot;Linux&quot;</span> <span class="ow">or</span> <span class="n">platform</span><span class="o">.</span><span class="n">architecture</span><span class="p">()[</span><span class="mi">0</span><span class="p">]</span> <span class="o">!=</span> <span class="s2">&quot;64bit&quot;</span><span class="p">:</span>
        <span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span>
            <span class="sa">f</span><span class="s2">&quot;Cross compile for windows is only supported on x86-64 Linux architecture, current platform: </span><span class="si">{</span><span class="n">platform</span><span class="o">.</span><span class="n">system</span><span class="p">()</span><span class="si">=}</span><span class="s2">, </span><span class="si">{</span><span class="n">platform</span><span class="o">.</span><span class="n">architecture</span><span class="p">()[</span><span class="mi">0</span><span class="p">]</span><span class="si">=}</span><span class="s2">&quot;</span>
        <span class="p">)</span>

    <span class="k">if</span> <span class="ow">not</span> <span class="n">file_path</span><span class="p">:</span>
        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;File path cannot be empty. Please provide a valid file path&quot;</span><span class="p">)</span>

    <span class="n">enabled_precisions_set</span><span class="p">:</span> <span class="n">Set</span><span class="p">[</span><span class="n">dtype</span> <span class="o">|</span> <span class="n">torch</span><span class="o">.</span><span class="n">dtype</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span>
        <span class="n">enabled_precisions</span>
        <span class="k">if</span> <span class="n">enabled_precisions</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span>
        <span class="k">else</span> <span class="n">_defaults</span><span class="o">.</span><span class="n">ENABLED_PRECISIONS</span>
    <span class="p">)</span>

    <span class="c1"># Prepare torch and torchtrt inputs</span>
    <span class="k">if</span> <span class="n">arg_inputs</span> <span class="ow">is</span> <span class="kc">None</span> <span class="ow">and</span> <span class="n">inputs</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
        <span class="k">raise</span> <span class="ne">AssertionError</span><span class="p">(</span><span class="s2">&quot;&#39;arg_inputs&#39; and &#39;inputs&#39; should not both be None.&quot;</span><span class="p">)</span>

    <span class="k">elif</span> <span class="n">arg_inputs</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="n">inputs</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
        <span class="k">raise</span> <span class="ne">AssertionError</span><span class="p">(</span>
            <span class="s2">&quot;&#39;arg_inputs&#39; and &#39;inputs&#39; should not be used at the same time.&quot;</span>
        <span class="p">)</span>

    <span class="n">arg_inputs</span> <span class="o">=</span> <span class="n">inputs</span> <span class="ow">or</span> <span class="n">arg_inputs</span>

    <span class="k">if</span> <span class="n">kwarg_inputs</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
        <span class="n">kwarg_inputs</span> <span class="o">=</span> <span class="p">{}</span>

    <span class="kn">from</span><span class="w"> </span><span class="nn">torch_tensorrt.dynamo.utils</span><span class="w"> </span><span class="kn">import</span> <span class="n">prepare_inputs</span>

    <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">arg_inputs</span><span class="p">,</span> <span class="n">collections</span><span class="o">.</span><span class="n">abc</span><span class="o">.</span><span class="n">Sequence</span><span class="p">):</span>
        <span class="n">arg_inputs</span> <span class="o">=</span> <span class="p">[</span><span class="n">arg_inputs</span><span class="p">]</span>  <span class="c1"># type: ignore</span>

    <span class="c1"># Export the module</span>
    <span class="n">torchtrt_arg_inputs</span> <span class="o">=</span> <span class="n">prepare_inputs</span><span class="p">(</span><span class="n">arg_inputs</span><span class="p">)</span>
    <span class="n">torchtrt_kwarg_inputs</span> <span class="o">=</span> <span class="n">prepare_inputs</span><span class="p">(</span><span class="n">kwarg_inputs</span><span class="p">)</span>

    <span class="n">exp_program</span> <span class="o">=</span> <span class="n">dynamo_trace</span><span class="p">(</span>
        <span class="n">module</span><span class="p">,</span> <span class="n">torchtrt_arg_inputs</span><span class="p">,</span> <span class="n">kwarg_inputs</span><span class="o">=</span><span class="n">torchtrt_kwarg_inputs</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span>
    <span class="p">)</span>
    <span class="n">logger</span><span class="o">.</span><span class="n">debug</span><span class="p">(</span><span class="s2">&quot;successfully exported the module&quot;</span><span class="p">)</span>

    <span class="c1"># Compile and save the module</span>
    <span class="n">trt_gm</span> <span class="o">=</span> <span class="n">dynamo_cross_compile_for_windows</span><span class="p">(</span>
        <span class="n">exp_program</span><span class="p">,</span>
        <span class="n">arg_inputs</span><span class="o">=</span><span class="n">torchtrt_arg_inputs</span><span class="p">,</span>
        <span class="n">enabled_precisions</span><span class="o">=</span><span class="n">enabled_precisions_set</span><span class="p">,</span>
        <span class="o">**</span><span class="n">kwargs</span><span class="p">,</span>
    <span class="p">)</span>

    <span class="n">dynamo_save_cross_compiled_exported_program</span><span class="p">(</span><span class="n">trt_gm</span><span class="p">,</span> <span class="n">file_path</span><span class="p">)</span>
    <span class="n">logger</span><span class="o">.</span><span class="n">debug</span><span class="p">(</span><span class="s2">&quot;successfully compiled and saved the module for windows&quot;</span><span class="p">)</span>


<span class="k">def</span><span class="w"> </span><span class="nf">torch_compile</span><span class="p">(</span><span class="n">module</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">:</span> <span class="n">Any</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Any</span><span class="p">:</span>
<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd">    Returns a boxed model which is the output of torch.compile.</span>
<span class="sd">    This does not compile the model to TRT. Execute this model on</span>
<span class="sd">    sample inputs to compile the model to TRT.</span>
<span class="sd">    &quot;&quot;&quot;</span>
    <span class="kn">from</span><span class="w"> </span><span class="nn">torch_tensorrt.dynamo.backend</span><span class="w"> </span><span class="kn">import</span> <span class="n">torch_tensorrt_backend</span>

    <span class="c1"># TODO: Remove dynamic=False when SymInt Dynamic shape support is ready</span>
    <span class="n">boxed_fn</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">compile</span><span class="p">(</span>
        <span class="n">module</span><span class="p">,</span> <span class="n">backend</span><span class="o">=</span><span class="n">torch_tensorrt_backend</span><span class="p">,</span> <span class="n">dynamic</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">options</span><span class="o">=</span><span class="p">{</span><span class="o">**</span><span class="n">kwargs</span><span class="p">}</span>
    <span class="p">)</span>

    <span class="k">return</span> <span class="n">boxed_fn</span>


<div class="viewcode-block" id="convert_method_to_trt_engine"><a class="viewcode-back" href="../../py_api/torch_tensorrt.html#torch_tensorrt.convert_method_to_trt_engine">[docs]</a><span class="k">def</span><span class="w"> </span><span class="nf">convert_method_to_trt_engine</span><span class="p">(</span>
    <span class="n">module</span><span class="p">:</span> <span class="n">Any</span><span class="p">,</span>
    <span class="n">method_name</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;forward&quot;</span><span class="p">,</span>
    <span class="n">inputs</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">Sequence</span><span class="p">[</span><span class="n">Input</span> <span class="o">|</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">]]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
    <span class="n">arg_inputs</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">Sequence</span><span class="p">[</span><span class="n">Sequence</span><span class="p">[</span><span class="n">Any</span><span class="p">]]]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
    <span class="n">kwarg_inputs</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">dict</span><span class="p">[</span><span class="n">Any</span><span class="p">,</span> <span class="n">Any</span><span class="p">]]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
    <span class="n">ir</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;default&quot;</span><span class="p">,</span>
    <span class="n">enabled_precisions</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">Set</span><span class="p">[</span><span class="n">Union</span><span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">dtype</span><span class="p">,</span> <span class="n">dtype</span><span class="p">]]]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
    <span class="o">**</span><span class="n">kwargs</span><span class="p">:</span> <span class="n">Any</span><span class="p">,</span>
<span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">bytes</span><span class="p">:</span>
<span class="w">    </span><span class="sd">&quot;&quot;&quot;Convert a TorchScript module method to a serialized TensorRT engine</span>

<span class="sd">    Converts a specified method of a module to a serialized TensorRT engine given a dictionary of conversion settings</span>

<span class="sd">    Arguments:</span>
<span class="sd">        module (Union(torch.nn.Module,torch.jit.ScriptModule): Source module</span>

<span class="sd">    Keyword Arguments:</span>
<span class="sd">        inputs (List[Union(torch_tensorrt.Input, torch.Tensor)]): **Required** List of specifications of input shape, dtype and memory layout for inputs to the module. This argument is required. Input Sizes can be specified as torch sizes, tuples or lists. dtypes can be specified using</span>
<span class="sd">            torch datatypes or torch_tensorrt datatypes and you can use either torch devices or the torch_tensorrt device type enum</span>
<span class="sd">            to select device type. ::</span>

<span class="sd">                input=[</span>
<span class="sd">                    torch_tensorrt.Input((1, 3, 224, 224)), # Static NCHW input shape for input #1</span>
<span class="sd">                    torch_tensorrt.Input(</span>
<span class="sd">                        min_shape=(1, 224, 224, 3),</span>
<span class="sd">                        opt_shape=(1, 512, 512, 3),</span>
<span class="sd">                        max_shape=(1, 1024, 1024, 3),</span>
<span class="sd">                        dtype=torch.int32</span>
<span class="sd">                        format=torch.channel_last</span>
<span class="sd">                    ), # Dynamic input shape for input #2</span>
<span class="sd">                    torch.randn((1, 3, 224, 244)) # Use an example tensor and let torch_tensorrt infer settings</span>
<span class="sd">                ]</span>

<span class="sd">        arg_inputs (Tuple[Any, ...]): Same as inputs. Alias for better understanding with kwarg_inputs.</span>
<span class="sd">        kwarg_inputs (dict[Any, ...]): Optional, kwarg inputs to the module forward function.</span>
<span class="sd">        enabled_precision (Set(Union(torch.dtype, torch_tensorrt.dtype))): The set of datatypes that TensorRT can use when selecting kernels</span>
<span class="sd">        ir (str): The requested strategy to compile. (Options: default - Let Torch-TensorRT decide, ts - TorchScript with scripting path)</span>
<span class="sd">        **kwargs: Additional settings for the specific requested strategy (See submodules for more info)</span>
<span class="sd">    Returns:</span>
<span class="sd">        bytes: Serialized TensorRT engine, can either be saved to a file or deserialized via TensorRT APIs</span>
<span class="sd">    &quot;&quot;&quot;</span>
    <span class="n">enabled_precisions_set</span> <span class="o">=</span> <span class="p">(</span>
        <span class="n">enabled_precisions</span> <span class="k">if</span> <span class="n">enabled_precisions</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="k">else</span> <span class="p">{</span><span class="n">torch</span><span class="o">.</span><span class="n">float</span><span class="p">}</span>
    <span class="p">)</span>

    <span class="k">if</span> <span class="n">arg_inputs</span> <span class="ow">is</span> <span class="kc">None</span> <span class="ow">and</span> <span class="n">inputs</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
        <span class="k">raise</span> <span class="ne">AssertionError</span><span class="p">(</span><span class="s2">&quot;&#39;arg_inputs&#39; and &#39;inputs&#39; should not both be None.&quot;</span><span class="p">)</span>

    <span class="k">elif</span> <span class="n">arg_inputs</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="n">inputs</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
        <span class="k">raise</span> <span class="ne">AssertionError</span><span class="p">(</span>
            <span class="s2">&quot;&#39;arg_inputs&#39; and &#39;inputs&#39; should not be used at the same time.&quot;</span>
        <span class="p">)</span>
    <span class="n">arg_inputs</span> <span class="o">=</span> <span class="n">arg_inputs</span> <span class="ow">or</span> <span class="n">inputs</span>

    <span class="n">module_type</span> <span class="o">=</span> <span class="n">_parse_module_type</span><span class="p">(</span><span class="n">module</span><span class="p">)</span>
    <span class="n">target_ir</span> <span class="o">=</span> <span class="n">_get_target_fe</span><span class="p">(</span><span class="n">module_type</span><span class="p">,</span> <span class="n">ir</span><span class="p">)</span>
    <span class="k">if</span> <span class="n">target_ir</span> <span class="o">==</span> <span class="n">_IRType</span><span class="o">.</span><span class="n">ts</span><span class="p">:</span>
        <span class="n">ts_mod</span> <span class="o">=</span> <span class="n">module</span>
        <span class="k">if</span> <span class="n">module_type</span> <span class="o">==</span> <span class="n">_ModuleType</span><span class="o">.</span><span class="n">nn</span><span class="p">:</span>
            <span class="n">logger</span><span class="o">.</span><span class="n">info</span><span class="p">(</span>
                <span class="s2">&quot;Module was provided as a torch.nn.Module, trying to script the module with torch.jit.script. In the event of a failure please preconvert your module to TorchScript&quot;</span>
            <span class="p">)</span>
            <span class="n">ts_mod</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">jit</span><span class="o">.</span><span class="n">script</span><span class="p">(</span><span class="n">module</span><span class="p">)</span>
        <span class="n">serialized_engine</span><span class="p">:</span> <span class="nb">bytes</span> <span class="o">=</span> <span class="n">ts_convert_method_to_trt_engine</span><span class="p">(</span>
            <span class="n">ts_mod</span><span class="p">,</span>
            <span class="n">inputs</span><span class="o">=</span><span class="n">arg_inputs</span><span class="p">,</span>
            <span class="n">method_name</span><span class="o">=</span><span class="n">method_name</span><span class="p">,</span>
            <span class="n">enabled_precisions</span><span class="o">=</span><span class="n">enabled_precisions_set</span><span class="p">,</span>
            <span class="o">**</span><span class="n">kwargs</span><span class="p">,</span>
        <span class="p">)</span>
        <span class="k">return</span> <span class="n">serialized_engine</span>
    <span class="k">elif</span> <span class="n">target_ir</span> <span class="o">==</span> <span class="n">_IRType</span><span class="o">.</span><span class="n">fx</span><span class="p">:</span>
        <span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span>
            <span class="s2">&quot;convert_method_to_trt_engine call is not supported for ir=fx&quot;</span>
        <span class="p">)</span>
    <span class="k">elif</span> <span class="n">target_ir</span> <span class="o">==</span> <span class="n">_IRType</span><span class="o">.</span><span class="n">dynamo</span><span class="p">:</span>
        <span class="c1"># Prepare torch and torchtrt inputs</span>
        <span class="k">if</span> <span class="n">kwarg_inputs</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
            <span class="n">kwarg_inputs</span> <span class="o">=</span> <span class="p">{}</span>

        <span class="kn">from</span><span class="w"> </span><span class="nn">torch_tensorrt.dynamo.utils</span><span class="w"> </span><span class="kn">import</span> <span class="n">prepare_inputs</span>

        <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">arg_inputs</span><span class="p">,</span> <span class="n">collections</span><span class="o">.</span><span class="n">abc</span><span class="o">.</span><span class="n">Sequence</span><span class="p">):</span>
            <span class="n">arg_inputs</span> <span class="o">=</span> <span class="p">[</span><span class="n">arg_inputs</span><span class="p">]</span>  <span class="c1"># type: ignore</span>

        <span class="c1"># Export the module</span>
        <span class="n">torchtrt_arg_inputs</span> <span class="o">=</span> <span class="n">prepare_inputs</span><span class="p">(</span><span class="n">arg_inputs</span><span class="p">)</span>
        <span class="n">torchtrt_kwarg_inputs</span> <span class="o">=</span> <span class="n">prepare_inputs</span><span class="p">(</span><span class="n">kwarg_inputs</span><span class="p">)</span>

        <span class="n">exp_program</span> <span class="o">=</span> <span class="n">torch_tensorrt</span><span class="o">.</span><span class="n">dynamo</span><span class="o">.</span><span class="n">trace</span><span class="p">(</span>
            <span class="n">module</span><span class="p">,</span> <span class="n">torchtrt_arg_inputs</span><span class="p">,</span> <span class="n">kwarg_inputs</span><span class="o">=</span><span class="n">torchtrt_kwarg_inputs</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span>
        <span class="p">)</span>

        <span class="k">return</span> <span class="n">dynamo_convert_exported_program_to_serialized_trt_engine</span><span class="p">(</span>
            <span class="n">exp_program</span><span class="p">,</span>
            <span class="n">arg_inputs</span><span class="o">=</span><span class="nb">tuple</span><span class="p">(</span><span class="n">arg_inputs</span><span class="p">),</span>
            <span class="n">kwarg_inputs</span><span class="o">=</span><span class="n">torchtrt_kwarg_inputs</span><span class="p">,</span>
            <span class="n">enabled_precisions</span><span class="o">=</span><span class="n">enabled_precisions_set</span><span class="p">,</span>
            <span class="o">**</span><span class="n">kwargs</span><span class="p">,</span>
        <span class="p">)</span>
    <span class="k">elif</span> <span class="n">target_ir</span> <span class="o">==</span> <span class="n">_IRType</span><span class="o">.</span><span class="n">torch_compile</span><span class="p">:</span>
        <span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span>
            <span class="s2">&quot;convert_method_to_trt_engine call is not supported for ir=torch_compile&quot;</span>
        <span class="p">)</span>
    <span class="k">else</span><span class="p">:</span>
        <span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span><span class="s2">&quot;Module is an unknown format or the ir requested is unknown&quot;</span><span class="p">)</span></div>


<div class="viewcode-block" id="load_cross_compiled_exported_program"><a class="viewcode-back" href="../../py_api/torch_tensorrt.html#torch_tensorrt.load_cross_compiled_exported_program">[docs]</a><span class="k">def</span><span class="w"> </span><span class="nf">load_cross_compiled_exported_program</span><span class="p">(</span><span class="n">file_path</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;&quot;</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Any</span><span class="p">:</span>
<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd">    Load an ExportedProgram file in Windows which was previously cross compiled in Linux</span>

<span class="sd">    Arguments:</span>
<span class="sd">        file_path (str): Path to file on the disk</span>

<span class="sd">    Raises:</span>
<span class="sd">        ValueError: If the api is not called in windows or there is no file or the file is not a valid ExportedProgram file</span>
<span class="sd">    &quot;&quot;&quot;</span>
    <span class="k">return</span> <span class="n">dynamo_load_cross_compiled_exported_program</span><span class="p">(</span><span class="n">file_path</span><span class="p">)</span></div>


<div class="viewcode-block" id="load"><a class="viewcode-back" href="../../py_api/torch_tensorrt.html#torch_tensorrt.load">[docs]</a><span class="k">def</span><span class="w"> </span><span class="nf">load</span><span class="p">(</span><span class="n">file_path</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;&quot;</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Any</span><span class="p">:</span>
<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd">    Load either a Torchscript model or ExportedProgram.</span>

<span class="sd">    Loads a TorchScript or ExportedProgram file from disk. File type will be detect the type using try, except.</span>

<span class="sd">    Arguments:</span>
<span class="sd">        file_path (str): Path to file on the disk</span>

<span class="sd">    Raises:</span>
<span class="sd">        ValueError: If there is no file or the file is not either a TorchScript file or ExportedProgram file</span>
<span class="sd">    &quot;&quot;&quot;</span>
    <span class="k">try</span><span class="p">:</span>
        <span class="n">logger</span><span class="o">.</span><span class="n">debug</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Loading the provided file </span><span class="si">{</span><span class="n">file_path</span><span class="si">}</span><span class="s2"> using torch.jit.load()&quot;</span><span class="p">)</span>
        <span class="n">ts_module</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">jit</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">file_path</span><span class="p">)</span>
        <span class="k">return</span> <span class="n">ts_module</span>
    <span class="k">except</span> <span class="ne">Exception</span><span class="p">:</span>
        <span class="n">logger</span><span class="o">.</span><span class="n">info</span><span class="p">(</span>
            <span class="sa">f</span><span class="s2">&quot;Loading the provided file </span><span class="si">{</span><span class="n">file_path</span><span class="si">}</span><span class="s2"> via torch.jit.load() failed with the following error&quot;</span><span class="p">,</span>
            <span class="n">exc_info</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
        <span class="p">)</span>
        <span class="k">pass</span>

    <span class="k">try</span><span class="p">:</span>
        <span class="n">logger</span><span class="o">.</span><span class="n">debug</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Loading the provided file </span><span class="si">{</span><span class="n">file_path</span><span class="si">}</span><span class="s2"> using torch.export.load()&quot;</span><span class="p">)</span>
        <span class="n">exp_program</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">export</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">file_path</span><span class="p">)</span>
        <span class="k">return</span> <span class="n">exp_program</span>
    <span class="k">except</span> <span class="ne">Exception</span><span class="p">:</span>
        <span class="n">logger</span><span class="o">.</span><span class="n">info</span><span class="p">(</span>
            <span class="sa">f</span><span class="s2">&quot;Loading the provided file </span><span class="si">{</span><span class="n">file_path</span><span class="si">}</span><span class="s2"> via torch.export.load() failed with the following error&quot;</span><span class="p">,</span>
            <span class="n">exc_info</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
        <span class="p">)</span>
        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
            <span class="sa">f</span><span class="s2">&quot;The file </span><span class="si">{</span><span class="n">file_path</span><span class="si">}</span><span class="s2"> doesn&#39;t correspond to a valid Torchscript module or ExportedProgram. Please verify the file path.&quot;</span>
        <span class="p">)</span></div>


<div class="viewcode-block" id="save"><a class="viewcode-back" href="../../py_api/torch_tensorrt.html#torch_tensorrt.save">[docs]</a><span class="k">def</span><span class="w"> </span><span class="nf">save</span><span class="p">(</span>
    <span class="n">module</span><span class="p">:</span> <span class="n">Any</span><span class="p">,</span>
    <span class="n">file_path</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;&quot;</span><span class="p">,</span>
    <span class="o">*</span><span class="p">,</span>
    <span class="n">output_format</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;exported_program&quot;</span><span class="p">,</span>
    <span class="n">inputs</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">Sequence</span><span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">]]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
    <span class="n">arg_inputs</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">Sequence</span><span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">]]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
    <span class="n">kwarg_inputs</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="n">Any</span><span class="p">]]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
    <span class="n">retrace</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span>
    <span class="n">pickle_protocol</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">2</span><span class="p">,</span>
    <span class="o">**</span><span class="n">kwargs</span><span class="p">:</span> <span class="n">Any</span><span class="p">,</span>
<span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd">    Save the model to disk in the specified output format.</span>

<span class="sd">    Arguments:</span>
<span class="sd">        module (Optional(torch.jit.ScriptModule | torch.export.ExportedProgram | torch.fx.GraphModule | CudaGraphsTorchTensorRTModule)): Compiled Torch-TensorRT module</span>
<span class="sd">        inputs (torch.Tensor): Torch input tensors</span>
<span class="sd">        arg_inputs (Tuple[Any, ...]): Same as inputs. Alias for better understanding with kwarg_inputs.</span>
<span class="sd">        kwarg_inputs (dict[Any, ...]): Optional, kwarg inputs to the module forward function.</span>
<span class="sd">        output_format (str): Format to save the model. Options include exported_program | torchscript | aot_inductor.</span>
<span class="sd">        retrace (bool): When the module type is a fx.GraphModule, this option re-exports the graph using torch.export.export(strict=False) to save it.</span>
<span class="sd">                This flag is experimental for now.</span>
<span class="sd">        pickle_protocol (int): The pickle protocol to use to save the model. Default is 2. Increase this to 4 or higher for large models</span>
<span class="sd">    &quot;&quot;&quot;</span>
    <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">module</span><span class="p">,</span> <span class="n">CudaGraphsTorchTensorRTModule</span><span class="p">):</span>
        <span class="n">module</span> <span class="o">=</span> <span class="n">module</span><span class="o">.</span><span class="n">compiled_module</span>
    <span class="n">module_type</span> <span class="o">=</span> <span class="n">_parse_module_type</span><span class="p">(</span><span class="n">module</span><span class="p">)</span>
    <span class="n">accepted_formats</span> <span class="o">=</span> <span class="p">{</span><span class="s2">&quot;exported_program&quot;</span><span class="p">,</span> <span class="s2">&quot;torchscript&quot;</span><span class="p">,</span> <span class="s2">&quot;aot_inductor&quot;</span><span class="p">}</span>
    <span class="k">if</span> <span class="n">arg_inputs</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="ow">not</span> <span class="nb">all</span><span class="p">(</span>
        <span class="nb">isinstance</span><span class="p">(</span><span class="nb">input</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">)</span> <span class="k">for</span> <span class="nb">input</span> <span class="ow">in</span> <span class="n">arg_inputs</span>
    <span class="p">):</span>
        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
            <span class="s2">&quot;Not all inputs provided are torch.tensors. Please provide torch.tensors as inputs&quot;</span>
        <span class="p">)</span>
    <span class="k">if</span> <span class="n">arg_inputs</span> <span class="ow">and</span> <span class="n">inputs</span><span class="p">:</span>
        <span class="k">raise</span> <span class="ne">AssertionError</span><span class="p">(</span>
            <span class="s2">&quot;&#39;arg_inputs&#39; and &#39;inputs&#39; should not be used at the same time.&quot;</span>
        <span class="p">)</span>

    <span class="n">arg_inputs</span> <span class="o">=</span> <span class="n">inputs</span> <span class="ow">or</span> <span class="n">arg_inputs</span>

    <span class="k">if</span> <span class="n">kwarg_inputs</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
        <span class="n">kwarg_inputs</span> <span class="o">=</span> <span class="p">{}</span>

    <span class="k">if</span> <span class="n">kwarg_inputs</span> <span class="ow">and</span> <span class="nb">any</span><span class="p">(</span><span class="n">value</span> <span class="ow">is</span> <span class="kc">None</span> <span class="k">for</span> <span class="n">value</span> <span class="ow">in</span> <span class="n">kwarg_inputs</span><span class="o">.</span><span class="n">values</span><span class="p">()):</span>
        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;kwargs should not include None.&quot;</span><span class="p">)</span>
    <span class="k">if</span> <span class="n">output_format</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">accepted_formats</span><span class="p">:</span>
        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
            <span class="sa">f</span><span class="s2">&quot;Provided output_format </span><span class="si">{</span><span class="n">output_format</span><span class="si">}</span><span class="s2"> is not supported. Supported options are exported_program | torchscript&quot;</span>
        <span class="p">)</span>
    <span class="k">if</span> <span class="n">output_format</span> <span class="o">==</span> <span class="s2">&quot;aot_inductor&quot;</span> <span class="ow">and</span> <span class="n">platform</span><span class="o">.</span><span class="n">system</span><span class="p">()</span> <span class="o">!=</span> <span class="s2">&quot;Linux&quot;</span><span class="p">:</span>
        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
            <span class="sa">f</span><span class="s2">&quot;The AOT Inductor format is only supported on Linux, </span><span class="si">{</span><span class="n">platform</span><span class="o">.</span><span class="n">system</span><span class="p">()</span><span class="si">}</span><span class="s2"> is not a supported platform for this format&quot;</span>
        <span class="p">)</span>
    <span class="k">if</span> <span class="ow">not</span> <span class="n">file_path</span><span class="p">:</span>
        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;File path cannot be empty. Please provide a valid file path&quot;</span><span class="p">)</span>

    <span class="k">if</span> <span class="n">module_type</span> <span class="o">==</span> <span class="n">_ModuleType</span><span class="o">.</span><span class="n">nn</span><span class="p">:</span>
        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
            <span class="s2">&quot;Input model is of type nn.Module. Saving nn.Module directly is not supported. Supported model types torch.jit.ScriptModule | torch.fx.GraphModule | torch.export.ExportedProgram.&quot;</span>
        <span class="p">)</span>
    <span class="k">elif</span> <span class="n">module_type</span> <span class="o">==</span> <span class="n">_ModuleType</span><span class="o">.</span><span class="n">ts</span><span class="p">:</span>
        <span class="k">if</span> <span class="ow">not</span> <span class="nb">all</span><span class="p">([</span><span class="n">output_format</span> <span class="o">==</span> <span class="n">f</span> <span class="k">for</span> <span class="n">f</span> <span class="ow">in</span> <span class="p">[</span><span class="s2">&quot;exported_program&quot;</span><span class="p">,</span> <span class="s2">&quot;aot_inductor&quot;</span><span class="p">]]):</span>
            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
                <span class="s2">&quot;Provided model is a torch.jit.ScriptModule but the output_format specified is not torchscript. Other output formats are not supported&quot;</span>
            <span class="p">)</span>
        <span class="k">else</span><span class="p">:</span>
            <span class="k">if</span> <span class="n">arg_inputs</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
                <span class="n">logger</span><span class="o">.</span><span class="n">warning</span><span class="p">(</span>
                    <span class="s2">&quot;Provided model is a torch.jit.ScriptModule, inputs or arg_inputs is not necessary during save.&quot;</span>
                <span class="p">)</span>
            <span class="n">torch</span><span class="o">.</span><span class="n">jit</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="n">module</span><span class="p">,</span> <span class="n">file_path</span><span class="p">)</span>
    <span class="k">elif</span> <span class="n">module_type</span> <span class="o">==</span> <span class="n">_ModuleType</span><span class="o">.</span><span class="n">ep</span><span class="p">:</span>
        <span class="k">if</span> <span class="n">output_format</span> <span class="o">==</span> <span class="s2">&quot;torchscript&quot;</span><span class="p">:</span>
            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
                <span class="s2">&quot;Provided model is a torch.export.ExportedProgram but the output_format specified is torchscript. Please verify the output_format&quot;</span>
            <span class="p">)</span>
        <span class="k">else</span><span class="p">:</span>
            <span class="k">if</span> <span class="n">arg_inputs</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
                <span class="n">logger</span><span class="o">.</span><span class="n">warning</span><span class="p">(</span>
                    <span class="s2">&quot;Provided model is a torch.export.ExportedProgram, inputs or arg_inputs is not necessary during save, it uses the inputs or arg_inputs provided during export and compile&quot;</span>
                <span class="p">)</span>
            <span class="k">if</span> <span class="n">output_format</span> <span class="o">==</span> <span class="s2">&quot;exported_program&quot;</span><span class="p">:</span>
                <span class="n">torch</span><span class="o">.</span><span class="n">export</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="n">module</span><span class="p">,</span> <span class="n">file_path</span><span class="p">,</span> <span class="n">pickle_protocol</span><span class="o">=</span><span class="n">pickle_protocol</span><span class="p">)</span>
            <span class="k">elif</span> <span class="n">output_format</span> <span class="o">==</span> <span class="s2">&quot;aot_inductor&quot;</span><span class="p">:</span>
                <span class="n">inductor_configs</span> <span class="o">=</span> <span class="p">{}</span>
                <span class="k">if</span> <span class="s2">&quot;inductor_configs&quot;</span> <span class="ow">in</span> <span class="n">kwargs</span><span class="p">:</span>
                    <span class="n">inductor_configs</span> <span class="o">=</span> <span class="n">kwargs</span><span class="p">[</span><span class="s2">&quot;inductor_configs&quot;</span><span class="p">]</span>

                <span class="n">torch</span><span class="o">.</span><span class="n">_inductor</span><span class="o">.</span><span class="n">aoti_compile_and_package</span><span class="p">(</span>
                    <span class="n">module</span><span class="p">,</span>
                    <span class="n">inductor_configs</span><span class="o">=</span><span class="n">inductor_configs</span><span class="p">,</span>
                    <span class="n">package_path</span><span class="o">=</span><span class="n">file_path</span><span class="p">,</span>
                <span class="p">)</span>
            <span class="k">else</span><span class="p">:</span>
                <span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span>
                    <span class="s2">&quot;Attempted to serialize an exported program with an unsupported format. Exported programs support exported_program and aot_inductor&quot;</span>
                <span class="p">)</span>
    <span class="k">elif</span> <span class="n">module_type</span> <span class="o">==</span> <span class="n">_ModuleType</span><span class="o">.</span><span class="n">fx</span><span class="p">:</span>
        <span class="c1"># The module type is torch.fx.GraphModule</span>
        <span class="k">if</span> <span class="n">output_format</span> <span class="o">==</span> <span class="s2">&quot;torchscript&quot;</span><span class="p">:</span>
            <span class="n">module_ts</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">jit</span><span class="o">.</span><span class="n">trace</span><span class="p">(</span>
                <span class="n">module</span><span class="p">,</span> <span class="n">arg_inputs</span><span class="p">,</span> <span class="n">example_kwarg_inputs</span><span class="o">=</span><span class="n">kwarg_inputs</span>
            <span class="p">)</span>
            <span class="n">torch</span><span class="o">.</span><span class="n">jit</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="n">module_ts</span><span class="p">,</span> <span class="n">file_path</span><span class="p">)</span>
        <span class="k">else</span><span class="p">:</span>
            <span class="k">if</span> <span class="ow">not</span> <span class="n">retrace</span><span class="p">:</span>
                <span class="kn">from</span><span class="w"> </span><span class="nn">torch_tensorrt.dynamo._exporter</span><span class="w"> </span><span class="kn">import</span> <span class="n">export</span>

                <span class="k">if</span> <span class="n">arg_inputs</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
                    <span class="n">logger</span><span class="o">.</span><span class="n">warning</span><span class="p">(</span>
                        <span class="s2">&quot;Provided model is a torch.fx.GraphModule and retrace is False, inputs or arg_inputs is not necessary during save.&quot;</span>
                    <span class="p">)</span>
                <span class="n">exp_program</span> <span class="o">=</span> <span class="n">export</span><span class="p">(</span><span class="n">module</span><span class="p">)</span>
                <span class="k">if</span> <span class="n">output_format</span> <span class="o">==</span> <span class="s2">&quot;exported_program&quot;</span><span class="p">:</span>
                    <span class="n">torch</span><span class="o">.</span><span class="n">export</span><span class="o">.</span><span class="n">save</span><span class="p">(</span>
                        <span class="n">exp_program</span><span class="p">,</span> <span class="n">file_path</span><span class="p">,</span> <span class="n">pickle_protocol</span><span class="o">=</span><span class="n">pickle_protocol</span>
                    <span class="p">)</span>
                <span class="k">elif</span> <span class="n">output_format</span> <span class="o">==</span> <span class="s2">&quot;aot_inductor&quot;</span><span class="p">:</span>
                    <span class="n">inductor_configs</span> <span class="o">=</span> <span class="p">{}</span>
                    <span class="k">if</span> <span class="s2">&quot;inductor_configs&quot;</span> <span class="ow">in</span> <span class="n">kwargs</span><span class="p">:</span>
                        <span class="n">inductor_configs</span> <span class="o">=</span> <span class="n">kwargs</span><span class="p">[</span><span class="s2">&quot;inductor_configs&quot;</span><span class="p">]</span>

                    <span class="n">torch</span><span class="o">.</span><span class="n">_inductor</span><span class="o">.</span><span class="n">aoti_compile_and_package</span><span class="p">(</span>
                        <span class="n">exp_program</span><span class="p">,</span>
                        <span class="n">inductor_configs</span><span class="o">=</span><span class="n">inductor_configs</span><span class="p">,</span>
                        <span class="n">package_path</span><span class="o">=</span><span class="n">file_path</span><span class="p">,</span>
                    <span class="p">)</span>
                <span class="k">else</span><span class="p">:</span>
                    <span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span>
                        <span class="s2">&quot;Attempted to serialize an exported program with an unsupported format. Exported programs support exported_program and aot_inductor&quot;</span>
                    <span class="p">)</span>
            <span class="k">else</span><span class="p">:</span>
                <span class="k">if</span> <span class="n">arg_inputs</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
                    <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
                        <span class="s2">&quot;Provided model is a torch.fx.GraphModule and retrace is True, however the inputs or arg_inputs are empty. Please provide valid torch.tensors as inputs or arg_inputs to trace and save the model&quot;</span>
                    <span class="p">)</span>
                <span class="n">exp_program</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">export</span><span class="o">.</span><span class="n">export</span><span class="p">(</span>
                    <span class="n">module</span><span class="p">,</span>
                    <span class="nb">tuple</span><span class="p">(</span><span class="n">arg_inputs</span><span class="p">),</span>
                    <span class="n">kwargs</span><span class="o">=</span><span class="n">kwarg_inputs</span><span class="p">,</span>
                    <span class="n">strict</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
                <span class="p">)</span>

                <span class="k">if</span> <span class="n">output_format</span> <span class="o">==</span> <span class="s2">&quot;exported_program&quot;</span><span class="p">:</span>
                    <span class="n">torch</span><span class="o">.</span><span class="n">export</span><span class="o">.</span><span class="n">save</span><span class="p">(</span>
                        <span class="n">exp_program</span><span class="p">,</span> <span class="n">file_path</span><span class="p">,</span> <span class="n">pickle_protocol</span><span class="o">=</span><span class="n">pickle_protocol</span>
                    <span class="p">)</span>
                <span class="k">elif</span> <span class="n">output_format</span> <span class="o">==</span> <span class="s2">&quot;aot_inductor&quot;</span><span class="p">:</span>
                    <span class="n">inductor_configs</span> <span class="o">=</span> <span class="p">{}</span>
                    <span class="k">if</span> <span class="s2">&quot;inductor_configs&quot;</span> <span class="ow">in</span> <span class="n">kwargs</span><span class="p">:</span>
                        <span class="n">inductor_configs</span> <span class="o">=</span> <span class="n">kwargs</span><span class="p">[</span><span class="s2">&quot;inductor_configs&quot;</span><span class="p">]</span>

                    <span class="n">torch</span><span class="o">.</span><span class="n">_inductor</span><span class="o">.</span><span class="n">aoti_compile_and_package</span><span class="p">(</span>
                        <span class="n">exp_program</span><span class="p">,</span>
                        <span class="n">inductor_configs</span><span class="o">=</span><span class="n">inductor_configs</span><span class="p">,</span>
                        <span class="n">package_path</span><span class="o">=</span><span class="n">file_path</span><span class="p">,</span>
                    <span class="p">)</span>
                <span class="k">else</span><span class="p">:</span>
                    <span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span>
                        <span class="s2">&quot;Attempted to serialize an exported program with an unsupported format. Exported programs support exported_program and aot_inductor&quot;</span>
                    <span class="p">)</span></div>
</pre></div>

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